We present experimental results comparing PPR-Bernoulli with other stopping rules from the literature. The predominant approach is to construct lower and upper confidence bounds on \(p_{1}\)  that hold with probability \(1 - \delta _{t}\)  for each \(t \ge 1\) , satisfying \(\sum _{t = 0}^{\infty } \delta _{t} \le \delta \) . The \(\delta \) -correctness of the procedure is ensured by terminating only when the lower confidence bound exceeds \(\frac{1}{2}\)  or the upper confidence bound falls below \(\frac{1}{2}\) . A common choice is to set \(\delta _{t} = k \frac{\delta }{t^{\alpha }}\) , with constants \(k\)  and \(\alpha \)  tuned for efficiency while ensuring \(\delta \) -correctness. As representatives of this approach, we pick the LUCB and KL-LUCB algorithms of Kaufmann and Kalyanakrishnan [1]}. The former inverts Hoeffding's inequality to obtain lower and upper confidence bounds, while the latter uses a tighter Chernoff bound. Although these algorithms themselves are meant for bandit applications, their efficiency crucially depends on the tightness of the confidence bounds applied to each arm. The tuned confidence bounds [1]} therefore become suitable baselines for our comparison. Details of our implementation of the LUCB and KL-LUCB confidence bounds, as well as forthcoming ones, are provided in Appendix . Links to code and related instructions are given in Appendix .
